Brazil

Text classification is an essential task in Natural Language Processing. The researchers and developers need data in the desired language to build new models and algorithms to develop this task. In this presentation, I discuss and make available three datasets of text classification in Portuguese based on data of the B2W group, which is an important contribution to the research in the field.

PROPOR 2022

Should I buy or should I pass: E-Commerce datasets in Portuguese

portuguese dataset

text classification

dataset

The AACL, Asia-Pacific Chapter of the Association for Computational Linguistics, and the AFNLP, Asian Federation of Natural Language Processing, invite you to participate in the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) and the 10th International Joint Conference on Natural Language Processing (IJCNLP). 
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A registration is required to access this event. Access to the event site will be granted to all registered attendees on **November 16th**. If you have not already done so, please register using the link below:
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<br>https://conference.iis.sinica.edu.tw/servlet/Register?ConferenceID=411

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IJCNLP-AACL 2022

The AACL, Asia-Pacific Chapter of the Association for Computational Linguistics, and the AFNLP, Asian Federation of Natural Language Processing, invite you to participate in the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) and the 10th International Joint Conference on Natural Language Processing (IJCNLP). 

poster

The International Conference on Computational Processing of Portuguese (PROPOR) is the main event in the area of human language processing that is focused on theoretical and technological issues of written and spoken Portuguese and Galician. The meeting has been a very rich forum for the exchange of ideas and partnerships for the research and industry communities dedicated to the automated processing of Portuguese, promoting the development of methodologies, resources and projects that can be shared among researchers and practitioners in the field.

Find more information and pay the registration fee on [event organizer’s website](https://sites.universidadedefortaleza.com/propor2022/?page_id=401)

The International Conference on Computational Processing of Portuguese (PROPOR) is the main event in the area of human language processing that is focused on theoretical and technological issues of written and spoken Portuguese and Galician.

We propose a methodology to populate the verb type hierarchy of a deep computational grammar in the HPSG formalism using syntactic and morphological information from Universal Dependencies (UD) treebanks. It is exemplified by means of the UD Bosque corpus and PorGram, a computational grammar for Portuguese constructed in the LinGO Grammar Matrix framework, but it can be applied to analogous grammars of other languages using other UD treebanks. The main component of the methodology is a Python module that extracts from the annotated sentences the core arguments and other features that are relevant to determine verb valence. This module enables the creation of a Python dictionary that maps valence frames to verb objects. This dictionary facilitates not only determining which frames occur with which verbs, but also detecting annotation errors. The potential of the module for rapid expansion of the lexical coverage of PorGram and corpus annotation error detection is illustrated with concrete examples.

Extracting valences from a dependency treebank for populating the verb lexicon of a Portuguese HPSG grammar

Fake news has become a research topic of great importance in Natural Language Processing due to its negative impact on our society. Although its pertinence, there are few datasets available in Brazilian Portuguese and mostly comprise few samples. Therefore, this paper proposes creating a new fake news dataset named FakeRecogna that contains a greater number of samples, more up-to-date news, and covering a few of the most important categories. We perform a toy evaluation over the created dataset using traditional classifiers such as Naive Bayes, Optimum-Path Forest, and Support Vector Machines. A Convolutional Neural Network is also evaluated in the context of fake news detection in the proposed dataset.

A New Brazilian Corpus for Fake News Detection

Most misinformation corpora are composed of explicitly real and fake news content. This results from the idea that misinformation can be approached as a binary classification problem. However, such approach oversimplifies the diversity of properties usually associated with credibility of different textual genres and types. To address this problem, we created MINT, a comprehensive annotated corpus of online articles collected from mainstream and independent Portuguese media sources, over a full year period. The collected articles include: hard news, opinion articles, soft news, satirical news, and conspiracy theories. This paper describes the main linguistic properties underlying each category, and provides some insights based on the analysis of an annotation initiative performed by online readers. The results show that (i) conspiracy theories and opinion articles present similar levels of subjectivity, and make use of fallacious arguments; (ii) irony and sarcasm are not only prevalent in satirical news, but also in conspiracy and opinion articles; and (iii) hard news differ from soft news by resorting to more sources of information, and presenting a higher degree of objectivity.

Mainstream and Independent News Text Corpus

Wepresent a new resource for discourse studies in Portuguese, the CRPC Discourse Bank (CRPC-DB). CRPC-DB follows the Penn Discourse Treebank style of annotation. The annotation is performed on the PAROLE corpus, a free subset of the Reference Corpus of Con temporary Portuguese (CRPC) that includes news, fiction and didactic/scientific texts. The discourse bank covers explicit and implicit relations at intra and inter-sentential levels, and includes for now a total of 14,436 discourse relations. We present the main guidelines of our annotation and discuss specific cases. An experiment in inter-annotator agreement was performed and holds results of 0.88 F1-score for discourse relation identification, 0.71 Cohen’s K for the classification of discourse relation types, and 0,75 for top-level sense classification. The CRPC-DB will be distributed free of charge through the PORTULAN CLARIN infrastructure.

]CRPC-DB A Discourse Bank for Portuguese

Although very useful, aspect-based sentiment analysis shows several challenges. The occurrence of implicit aspects is one of them. To improve our understanding of implicit aspects, this work analyzes their composition and categorizes the implicit aspect clues found in two corpora of opinionated texts of different domains in the Portuguese language. As results of this work, in addition to the implicit aspect typology, we present four lexicons of implicit aspect clues for different domains and the annotation of implicit aspect clues in an existing corpus.

Implicit opinion aspect clues in Portuguese texts: analysis and categorization

In the hospital environment, the incidence of adverse events (unforeseen incidents that cause harm to patients) is the primary concern of risk management teams. The use of machine learning techniques may help healthcare professional to identify and mitigate adverse events. This thesis develops two experiments to evaluate machine learning approaches to identify two major adverse events in electronic health records. In this extended abstract we cover the algorithm created to identify fall events in clinical notes, as they are closely related to the processing of portuguese language. We annotated 1,402 clinical sentences with fall events to train a Token Classifier (TkC) to detect words within the context of falls. The TkC was able to correctly identify 85% of the sentences with fall events.

Applying Machine Learning to Electronic Health Records: a Study on Two Adverse Events

Tackling Multilingual Media Monitoring with Natural Language Processing

From (Our) Past to (Our) Future with Natural Language Processing (NLP): Linguistic and Terminological Approaches to Digital Historical Epidemiology in Portuguese

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Extracting valences from a dependency treebank for populating the verb lexicon of a Portuguese HPSG grammar

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